Symbolic and Neuro-symbolic AI
Symbolic AI and Neuro-symbolic AI represent two significant paradigms in artificial intelligence, with the former focusing on rule-based reasoning and the latter combining logical inference with neural network capabilities. This comprehensive overview explores their principles, applications, and the emerging role of knowledge graphs in enhancing large language models.
Symbolic AI, also known as Good Old-Fashioned AI (GOFAI), operates on principles that mirror human reasoning through explicit symbols and logical rules. Unlike data-driven approaches, symbolic AI relies on carefully crafted rules to manipulate human-readable symbols, similar to how we use language and mathematics to represent and solve problems.
Neuro-symbolic AI represents a groundbreaking fusion of symbolic reasoning and neural networks, combining the strengths of both approaches to create more robust and versatile artificial intelligence systems. This hybrid architecture aims to address the limitations of each individual approach while leveraging their unique advantages.
Required Reading and Listening
Listen to the podcast:
Read the following:
- Summary Blog: Symbolic and Neuro-symbolic AI: An Overview
- Textbook: Textbook: Alexiei Dingli, David Farrugia, Neuro-Symbolic AI. Chapters 2, 4, 5, 7. Published by O’Reilly Media, Inc., ISBN-13 978-1098176495. This book is available in print and digital on O’Reilly Media. GSU Library Link
More resources can be found on the resource page Symbolic and Neuro-symbolic AI